scholarly journals Coverage Control Optimization Algorithm for Wireless Sensor Networks Based on Combinatorial Mathematics

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yongjie Wang ◽  
Maolin Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Completion Time ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Convergence Problem ◽  
Swarm Optimization ◽  
Coverage Control ◽  
Control Optimization

The traditional wireless sensor network coverage control optimization algorithm has the problems of long completion time, high energy consumption, and low coverage. A new algorithm based on combinational mathematics for wireless sensor network coverage control is proposed. The particle swarm optimization (PSO) algorithm is used to optimize the coverage control process of wireless sensor networks. Then, the combined mathematics method is used to detect the local convergence problem. Finally, the quasi-physical forces of quasi-gravity and Coulomb force are used to integrate the quasi-physical force into the particle. In the process of velocity evolution, the speed correction process of particle swarm optimization is optimized, which can effectively avoid the local convergence problem of the particle swarm optimization algorithm, reduce the repeated coverage, and expand the coverage. The experimental results show that compared with the traditional algorithm, the proposed algorithm has short completion time, low energy consumption, and high coverage.

scholarly journals Hybrid Wireless Sensor Network Coverage Holes Restoring Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Liu Zhouzhou ◽  
Yanhong She
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

Aiming at the perception hole caused by the necessary movement or failure of nodes in the wireless sensor actuator network, this paper proposed a kind of coverage restoring scheme based on hybrid particle swarm optimization algorithm. The scheme first introduced network coverage based on grids, transformed the coverage restoring problem into unconstrained optimization problem taking the network coverage as the optimization target, and then solved the optimization problem in the use of the hybrid particle swarm optimization algorithm with the idea of simulated annealing. Simulation results show that the probabilistic jumping property of simulated annealing algorithm could make up for the defect that particle swarm optimization algorithm is easy to fall into premature convergence, and the hybrid algorithm can effectively solve the coverage restoring problem.

scholarly journals WSN Node Optimal Deployment Algorithm Based on Adaptive Binary Particle Swarm Optimization

2021 ◽  
Vol 1 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Yujiang Li ◽  
Jinghua Cao
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Network Nodes ◽  
Optimal Deployment

In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introduced to obtain the optimal deployment algorithm of wireless sensor network nodes. The experimental results show that the algorithm can reduce the number of active nodes efficiently, improve coverage uniformity, reduce network energy consumption and prolong network lifetime under the premise that the coverage rate is greater than 90%, and compared with an algorithm called coverage configuration protocol, an algorithm called finding the minimum working sets in wireless sensor networks, and an algorithm called binary particle swarm optimization-g in literature, the number of active nodes in this algorithm is reduced by about 36%, 30% and 23% respectively.

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